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Speaker: Dr. Saeid Hedayatrasa, Flanders Make
Abstract:
The vibrational characteristics of structural components contain valuable insights into their inherent mechanical properties and overall health status. Consequently, there’s a pressing demand for efficient physics-based inversion algorithms. These algorithms must effectively reconstruct responses at unmeasured locations and/or identify unknown mechanical properties using a sparse set of noisy sensory data. Physics-informed neural networks (PINNs) offer promising solutions by seamlessly integrating the governing physics equations into their framework. Nonetheless, challenges arise due to the complex, multimodal, and multiscale nature of vibrational responses, along with the spectral bias of PINNs. In this talk we present a novel PINNs approach for efficient multimodal mapping of vibrations in a composite laminate and identification of its orthotropic elastic properties, given limited number of noisy simulation data points.
